基于GA-GRNN的疲劳驾驶检测

Fatigue Driving Detection Based on GA-GRNN

近些年来,人们为追求快捷舒适的生活环境促使了汽车行业的快速发展。汽车数量的增加导致了交通事故的频发,给人们造成了巨大的生命伤亡和财产损失。经调查发现在众多导致交通事故的因素中,疲劳驾驶是主要的原因之一。探索一种疲劳驾驶检测方法对于保护生命安全和减少财产损失是非常有意义的。这篇文章主要通过眼电信号进行疲劳驾驶检测。首先利用离散小波变换对眼电信号进行滤波,其次提取眼电信号的近似熵、排列熵和模糊熵的特征;最后使用遗传优化的广义回归神经网络进行疲劳驾驶状态检测。遗传优化的广义回归神经网络平均分类准确率为72.5%。实验结果表明可以通过提取眼电信号的近似熵、排列熵和模糊熵特征,然后在使用遗传优化的广义回归神经网络进行疲劳驾驶检测。

In recent years,people's pursuit of a fast and comfortable living environment has led to the rapid development of the automobile industry.The increase in the number of cars has led to frequent traffic accidents,which have caused huge casualties and property losses to people.The survey found that fatigue driving is one of the main reasons among many factors leading to traffic accidents.It is very meaningful to explore a fatigue driving detection method for protecting life safety and reducing property damage.This article focuses on fatigue driving detection through electrooculogram signals.Firstly,the discrete wavelet transform is used to filter the electrooculogram signals;secondly,the features of approximate entropy,permutation entropy and fuzzy entropy of the electrooculogram signals are extracted;finally,the generalized regression neural network based on genetic optimization is used for fatigue driving state detection.The average classification accuracy of the generalized regression neural network based on genetic optimization is 72.5%.The experimental results show that it is possible to extract the approximate entropy,permutation entropy and fuzzy entropy features of the electrooculogram signals,and then use a generalized regression neural network based on genetic optimization for fatigue driving detection.